Project description

Gradefact aims to establish a scoring system for news organizations, public figures, and anyone making predictions, similar to how credit scores evaluate an individual's creditworthiness. Our goal is to improve the accuracy and accountability of news and predictions through a transparent, objective grading system. This system will help reduce the spread of deliberately false or incompetent news and predictions, allowing for higher-quality, more truthful information. The system also algorithmically interprets a neutrality score (bias) and a political leaning when appropriate.

Gradefact's proprietary algorithm is able to infer a probabilistic prediction even when a publication or individual does not explicitly state a prediction. This is achieved through advanced natural language processing techniques that analyze the text or speech of the source and try to infer the implied probability of an event, albeit with lower weighting than binary predictions. For example, if a news organization writes an article about a political candidate's chances of winning an election, our system can determine the probability that the author believes the candidate will win, even if they do not explicitly state a prediction. This allows us to include more sources (editorials, opinion pieces, tweets etc) in our grading and provide a more comprehensive view of the accuracy of a publication or individual.

Gradefact integrates the predictions of our graded sources with for-profit prediction and betting markets. By initially aggregating these markets and eventually creating our own real-money prediction markets for major events, we aim to create accountability for both mainstream news sources and predictors. This means that both parties risk something when making a prediction: the sources risk their reputation, while Gradefact users risk capital. The defining ethos of the platform can be summarized by the saying “Put your money where your mouth is”.

What is your track record on similar projects?

Our team is consisting of a former professional poker player (ie full time human prediction machine), a former full time crypto/DeFi product designer/manager and full time Data Scientist and natural language processing expert. On aggregate the team members have launched startups (with varying degrees of success) in the Machine learning space, Decentralised Lending space, Gambling related space and others.

How will you spend your funding?

Our initial expenditure is focussed on annotation (we need a team of annotators to help train our system), initial UX and UI designs, and monthly expenditure on servers, specifically GPU architecture and machine learning hosting platforms. We also have expenses for hardware and licensing of various apis, feeds and aggregation software.